Ancestry_HMM-S

Ancestry_HMM-S infers and quantifies adaptive introgression in population genomic datasets to identify loci under introgression and estimate the strength of selection on introgressed alleles.

Key Features:

• Hidden Markov Model (HMM) framework: Employs a Hidden Markov Model to detect loci and genomic tracts undergoing adaptive introgression.
• Quantification of selection strength: Estimates the strength of selection acting on introgressed alleles at candidate loci.
• Validation and performance: Underwent extensive validation on moderately sized datasets with realistic population structures and selection parameters.

Scientific Applications:

• Admixed-population analysis: Applied to admixed populations to infer the genetic consequences of admixture and identify loci that have undergone adaptive introgression.
• Drosophila melanogaster case study: Identified 17 loci with signatures of adaptive introgression in a South African admixed population, including four loci previously associated with insecticide resistance.
• Studies of adaptive traits: Used to investigate adaptive introgression contributing to pesticide resistance, immune function, and local adaptation.

Methodology:

Detection and inference are performed using a Hidden Markov Model framework and include estimation of selection strength on introgressed alleles.